8h ago

Mixing Fine-Tuning Data Into Pretraining Retains Model Capabilities

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Original post
Aditi Raghunathan#1123@ADTRAGHUNATHANOPLawrence FengLawrence Feng@LAWRENCEFENG17

1/ To retain post-training capabilities after further fine-tuning, mix that data into pretraining. The effect can be invisible until fine-tuning begins; early exposure may not help post-training performance, but it changes what persists. How a model learns a task matters.

9:39 AM · May 19, 2026 View on X
Reposted by
Aditi Raghunathan#1123@ADTRAGHUNATHAN
QUOTE POSTPratyush Maini#1455Pratyush Maini@PRATYUSHMAINI

The evidence for specialized pretraining keeps growing.

This really nice study shows how early exposure leads to robustness to forgetting.

Enterprises serious about AI use cases should start thinking about training custom models from scratch, not just post-training or RL.

Lawrence FengLawrence Feng@lawrencefeng17

1/ To retain post-training capabilities after further fine-tuning, mix that data into pretraining. The effect can be invisible until fine-tuning begins; early exposure may not help post-training performance, but it changes what persists. How a model learns a task matters.

4:39 PM · May 19, 2026 · 6.4K Views
4:54 PM · May 19, 2026 · 2.2K Views
Mixing Fine-Tuning Data Into Pretraining Retains Model Capabilities · Digg